Note: This analysis requires input data generated by an earlier script.

Setup

  • Clear memory and console
  • Load packages
  • Get current script name
# Clear memory
rm(list=setdiff(ls(all=TRUE), c(".Random.seed")))
# Clear console
cat("\014")

Set up

this.script <- rstudioapi::getActiveDocumentContext()$path %>% basename
cat("Script:", this.script)
Script: 06_Figure_S1.Rmd
gg.get.breaks_by1 <- function(limits) {
  a <- floor(limits[1])
  b <- ceiling(limits[2])
  seq(a, b, by = 1)
}

gg.get.breaks_by2 <- function(limits) {
  a <- floor(limits[1])
  if (a %% 2 == 1) a <- a - 1
  b <- ceiling(limits[2])
  if (b %% 2 == 1) b <- b + 1
  seq(a, b, by = 2)
}
get.gg <- function(mtrx, colum.names, axis.labs, titre,
                    marker.col.false, marker.col.true="#B20000", marker.alpha=1, ttable) {

  ttable2 <- dplyr::select(ttable, PROBEID, SYMBOL, adj.P.Val) %>% 
    dplyr::group_by(PROBEID) %>%
    dplyr::slice(which.min(adj.P.Val)) %>% 
    dplyr::ungroup(.)

  df <- mtrx[,colum.names] %>% 
    magrittr::set_colnames(axis.labs) %>% 
    tibble::rownames_to_column("PROBEID")
  
  df2 <- merge(x=ttable2, y=df, by="PROBEID", all.x=FALSE, all.y=TRUE) %>% 
    dplyr::mutate(p.ok = adj.P.Val <= 0.05) %>% 
    dplyr::mutate(p.ok = as.character(p.ok)) %>% 
    dplyr::mutate(p.ok = factor(p.ok, levels=c("FALSE", "TRUE"))) %>% 
    dplyr::mutate(ID = paste0(SYMBOL, " (", PROBEID, ")")) %>% 
    dplyr::select(-PROBEID, -SYMBOL, -adj.P.Val) %>% 
    dplyr::select(ID, everything())

  res <- df2 %>% 
    ggplot(aes_string(x=axis.labs[1], y=axis.labs[2])) +
    geom_point(aes(text=ID, color=p.ok), alpha=marker.alpha) + # color=marker.col.false,
    # scale_color_discrete(name="p ≤ 0.05") +
    scale_color_manual(values=c(marker.col.false, marker.col.true), name="p ≤ 0.05") +
    scale_x_continuous(limits =c(min(mtrx), max(mtrx))) +
    scale_y_continuous(limits =c(min(mtrx), max(mtrx))) +
    geom_abline(intercept = 0, slope = 1, color="grey25") +
    geom_abline(intercept = -log2(1.75), slope = 1, color="grey25", linetype="dashed") +
    geom_abline(intercept = log2(1.75), slope = 1, color="grey25", linetype="dashed") +
    labs(title = titre)
  res
}

Load data

Load mean expression data

Table includes probe IDs, gene symbols and gene names.
Probe IDs and symbols are not unique (due to gene symbol mapping).

mean.expr.file <- "./data/01_Raw_Data_Processing.Rmd.expr_mean.txt"
matrix.df.mean <- read.table(mean.expr.file, sep="\t", header=T,
                             stringsAsFactors = FALSE) %>% 
  dplyr::select(-SYMBOL, -GENENAME) %>% 
  dplyr::distinct(PROBEID, .keep_all=TRUE) %>% 
  tibble::column_to_rownames("PROBEID")
cat("File read:", mean.expr.file)
File read: ./data/01_Raw_Data_Processing.Rmd.expr_mean.txt
rm(mean.expr.file)

Load differentially expressed probes (effect of MMS)

# list.files("data")
diff.probes.file <- "./data/02_Suppl_Table_01.Rmd.DEprobes.RDS"
stopifnot(file.exists(diff.probes.file))
test.results.probes <- readRDS(diff.probes.file)
cat("File read:", diff.probes.file)
File read: ./data/02_Suppl_Table_01.Rmd.DEprobes.RDS
rm(diff.probes.file)
x <- paste(utils::capture.output(str(test.results.probes)), collapse="<br>\n", sep="")
details::details(x, summary="Show summary of DE probes", lang=NULL)
Show summary of DE probes

List of 4
$ ko.ctrl.vs.wt.ctrl:List of 3
..$ down : chr [1:5] “1419620_at” “1424105_a_at” “1425771_at” “1438390_s_at” …
..$ up : chr [1:4] “1416958_at” “1426230_at” “1426464_at” “1438211_s_at”
..$ total: chr [1:9] “1416958_at” “1419620_at” “1424105_a_at” “1425771_at” …
$ ko.mms.vs.wt.mms :List of 3
..$ down : chr [1:3] “1419620_at” “1424105_a_at” “1438390_s_at”
..$ up : chr “1455530_at”
..$ total: chr [1:4] “1419620_at” “1424105_a_at” “1438390_s_at” “1455530_at”
$ wt.mms.vs.wt.ctrl :List of 3
..$ down : chr [1:584] “1415743_at” “1415802_at” “1415810_at” “1415822_at” …
..$ up : chr [1:232] “1416029_at” “1416108_a_at” “1416235_at” “1416432_at” …
..$ total: chr [1:816] “1415743_at” “1415802_at” “1415810_at” “1415822_at” …
$ ko.mms.vs.ko.ctrl :List of 3
..$ down : chr [1:105] “1415972_at” “1416158_at” “1417020_at” “1417073_a_at” …
..$ up : chr [1:115] “1415817_s_at” “1416100_at” “1416172_at” “1416347_at” …
..$ total: chr [1:220] “1415817_s_at” “1415972_at” “1416100_at” “1416158_at” …


rm(x)

Load toptables

# list.files()
tt.file <- "Supplemental_Data_1.xlsx"
stopifnot(file.exists(tt.file))
tt.sheet.names <- openxlsx::getSheetNames(tt.file)
# tt.sheet.names
# "ko.ctrl.vs.wt.ctrl" "ko.mms.vs.wt.mms"   "wt.mms.vs.wt.ctrl"  "ko.mms.vs.ko.ctrl"
get.xlsx.data <- function(fil, sheet) {
  openxlsx::read.xlsx(xlsxFile = fil, sheet=sheet)
}
tt.list <- lapply(tt.sheet.names, function(the.sheet) {
  res <- get.xlsx.data(fil=tt.file, sheet=the.sheet) %>% 
    dplyr::select(PROBEID, SYMBOL, logFC, adj.P.Val)
  res
}) %>% 
  set_names(tt.sheet.names)
cat("File read:", tt.file)
File read: Supplemental_Data_1.xlsx
rm(tt.file, tt.sheet.names)
y <- paste(utils::capture.output(str(tt.list)), collapse="<br>\n", sep="")
details::details(y, summary="Show summary of toptables list", lang=NULL)
Show summary of toptables list

List of 4
$ ko.ctrl.vs.wt.ctrl:‘data.frame’: 24491 obs. of 4 variables:
..$ PROBEID : chr [1:24491] “1439200_x_at” “1426464_at” “1438211_s_at” “1455265_a_at” …
..$ SYMBOL : chr [1:24491] “Rhox4b” “Nr1d1” “Dbp” “Rgs16” …
..$ logFC : num [1:24491] 4.19 3.6 3.36 3.33 3.26 …
..$ adj.P.Val: num [1:24491] 0.35717 0.00159 0.04513 0.41762 0.50034 …
$ ko.mms.vs.wt.mms :‘data.frame’: 24491 obs. of 4 variables:
..$ PROBEID : chr [1:24491] “1439200_x_at” “1415905_at” “1434137_x_at” “1448964_at” …
..$ SYMBOL : chr [1:24491] “Rhox4b” “Reg1” “Zg16” “S100g” …
..$ logFC : num [1:24491] 4 3.48 3.42 2.97 2.47 …
..$ adj.P.Val: num [1:24491] 0.0941 0.1387 0.2036 0.1737 0.3266 …
$ wt.mms.vs.wt.ctrl :‘data.frame’: 24491 obs. of 4 variables:
..$ PROBEID : chr [1:24491] “1438211_s_at” “1428942_at” “1455265_a_at” “1449233_at” …
..$ SYMBOL : chr [1:24491] “Dbp” “Mt2” “Rgs16” “Bhlha15” …
..$ logFC : num [1:24491] 4.33 4.23 4.2 4.17 4 …
..$ adj.P.Val: num [1:24491] 0.00214 0.00594 0.01936 0.02318 0.00214 …
$ ko.mms.vs.ko.ctrl :‘data.frame’: 24491 obs. of 4 variables:
..$ PROBEID : chr [1:24491] “1415905_at” “1448964_at” “1418287_a_at” “1434137_x_at” …
..$ SYMBOL : chr [1:24491] “Reg1” “S100g” “Dmbt1” “Zg16” …
..$ logFC : num [1:24491] 4.15 3.91 3.48 3.46 3.39 …
..$ adj.P.Val: num [1:24491] 0.0582 0.0638 0.0832 0.129 0.1372 …


rm(y)

Plot probes suppressed OR induced by MMS in wild-type OR knockout

Select probes to plot

wt.ko.down.up <- sort(unique(unlist(test.results.probes)))
stopifnot(!any(is.na(wt.ko.down.up)))
cat("Probes selected:", length(wt.ko.down.up))
Probes selected: 980
# matrix.df.mean[1:4,1:4]
mx.wt.ko.down.up <- matrix.df.mean[wt.ko.down.up,]
dim(mx.wt.ko.down.up)
gg.6.4 <- get.gg(mtrx=mx.wt.ko.down.up,
       colum.names = c("Wt_Ctrl", "Ko_Ctrl"),
       axis.labs = c("WT", "KO"),
       titre = "Control: Knockout versus Wild-type",
       marker.col.false = "#005AB5", # bp[3]
       marker.col.true = "#DC3220",
       marker.alpha=0.5,
       ttable=tt.list$ko.ctrl.vs.wt.ctrl)
gg.6.4b <- gg.6.4 + 
  theme_classic() +
  coord_equal()
ggplotly(gg.6.4b)
out.file.pdf <- "Figure_S1.pdf"
ggsave(filename=out.file.pdf, plot = gg.6.4b, width = 10, height=10, units = "cm")
cat("Saved:", out.file.pdf)
Saved: Figure_S1.pdf

Session info

cat("Date:", format(Sys.time(), "%a %d-%b-%Y %H:%M:%S"), "<br>\n")

Date: Sun 28-Mar-2021 22:49:25

devtools::session_info()
─ Session info ────────────────────────────────────────────────────────────────────────────────────────────────────

─ Packages ────────────────────────────────────────────────────────────────────────────────────────────────────────
 package        * version date       lib source        
 affy             1.64.0  2019-10-29 [1] Bioconductor  
 affyio           1.56.0  2019-10-29 [1] Bioconductor  
 AnnotationDbi  * 1.48.0  2019-10-29 [1] Bioconductor  
 assertthat       0.2.1   2019-03-21 [1] CRAN (R 3.6.1)
 Biobase        * 2.46.0  2019-10-29 [1] Bioconductor  
 BiocGenerics   * 0.32.0  2019-10-29 [1] Bioconductor  
 BiocManager      1.30.12 2021-03-28 [1] CRAN (R 3.6.1)
 bit              4.0.4   2020-08-04 [1] CRAN (R 3.6.1)
 bit64            4.0.5   2020-08-30 [1] CRAN (R 3.6.1)
 blob             1.2.1   2020-01-20 [1] CRAN (R 3.6.1)
 bslib            0.2.4   2021-01-25 [1] CRAN (R 3.6.1)
 cachem           1.0.4   2021-02-13 [1] CRAN (R 3.6.1)
 callr            3.6.0   2021-03-28 [1] CRAN (R 3.6.1)
 cellranger       1.1.0   2016-07-27 [1] CRAN (R 3.6.1)
 cli              2.3.1   2021-02-23 [1] CRAN (R 3.6.1)
 clipr            0.7.1   2020-10-08 [1] CRAN (R 3.6.1)
 codetools        0.2-16  2018-12-24 [2] CRAN (R 3.6.1)
 colorspace       2.0-0   2020-11-11 [1] CRAN (R 3.6.1)
 crayon           1.4.1   2021-02-08 [1] CRAN (R 3.6.1)
 crosstalk        1.1.1   2021-01-12 [1] CRAN (R 3.6.1)
 data.table       1.14.0  2021-02-21 [1] CRAN (R 3.6.1)
 DBI              1.1.1   2021-01-15 [1] CRAN (R 3.6.1)
 desc             1.3.0   2021-03-05 [1] CRAN (R 3.6.1)
 details          0.2.1   2020-01-12 [1] CRAN (R 3.6.1)
 devtools         2.3.2   2020-09-18 [1] CRAN (R 3.6.1)
 digest           0.6.27  2020-10-24 [1] CRAN (R 3.6.1)
 dplyr          * 1.0.5   2021-03-05 [1] CRAN (R 3.6.1)
 DT               0.17    2021-01-06 [1] CRAN (R 3.6.1)
 ellipsis         0.3.1   2020-05-15 [1] CRAN (R 3.6.1)
 evaluate         0.14    2019-05-28 [1] CRAN (R 3.6.1)
 fansi            0.4.2   2021-01-15 [1] CRAN (R 3.6.1)
 farver           2.1.0   2021-02-28 [1] CRAN (R 3.6.1)
 fastmap          1.1.0   2021-01-25 [1] CRAN (R 3.6.1)
 fs               1.5.0   2020-07-31 [1] CRAN (R 3.6.1)
 generics         0.1.0   2020-10-31 [1] CRAN (R 3.6.1)
 ggplot2        * 3.3.3   2020-12-30 [1] CRAN (R 3.6.1)
 glue             1.4.2   2020-08-27 [1] CRAN (R 3.6.1)
 gridExtra        2.3     2017-09-09 [1] CRAN (R 3.6.1)
 gtable           0.3.0   2019-03-25 [1] CRAN (R 3.6.1)
 htmltools        0.5.1.1 2021-01-22 [1] CRAN (R 3.6.1)
 htmlwidgets      1.5.3   2020-12-10 [1] CRAN (R 3.6.1)
 httr             1.4.2   2020-07-20 [1] CRAN (R 3.6.1)
 IRanges        * 2.20.2  2020-01-13 [1] Bioconductor  
 jquerylib        0.1.3   2020-12-17 [1] CRAN (R 3.6.1)
 jsonlite         1.7.2   2020-12-09 [1] CRAN (R 3.6.1)
 knitr            1.31    2021-01-27 [1] CRAN (R 3.6.1)
 labeling         0.4.2   2020-10-20 [1] CRAN (R 3.6.1)
 lazyeval         0.2.2   2019-03-15 [1] CRAN (R 3.6.1)
 lifecycle        1.0.0   2021-02-15 [1] CRAN (R 3.6.1)
 limma            3.42.2  2020-02-03 [1] Bioconductor  
 magrittr       * 2.0.1   2020-11-17 [1] CRAN (R 3.6.1)
 memoise          2.0.0   2021-01-26 [1] CRAN (R 3.6.1)
 mouse430a2.db  * 3.2.3   2021-03-28 [1] Bioconductor  
 mouse430a2cdf  * 2.18.0  2021-03-28 [1] Bioconductor  
 munsell          0.5.0   2018-06-12 [1] CRAN (R 3.6.1)
 openxlsx         4.2.3   2020-10-27 [1] CRAN (R 3.6.1)
 org.Mm.eg.db   * 3.10.0  2021-03-28 [1] Bioconductor  
 pillar           1.5.1   2021-03-05 [1] CRAN (R 3.6.1)
 pkgbuild         1.2.0   2020-12-15 [1] CRAN (R 3.6.1)
 pkgconfig        2.0.3   2019-09-22 [1] CRAN (R 3.6.1)
 pkgload          1.2.0   2021-02-23 [1] CRAN (R 3.6.1)
 plotly         * 4.9.3   2021-01-10 [1] CRAN (R 3.6.1)
 png              0.1-7   2013-12-03 [1] CRAN (R 3.6.1)
 preprocessCore   1.48.0  2019-10-29 [1] Bioconductor  
 prettyunits      1.1.1   2020-01-24 [1] CRAN (R 3.6.1)
 processx         3.5.0   2021-03-23 [1] CRAN (R 3.6.1)
 pryr             0.1.4   2018-02-18 [1] CRAN (R 3.6.1)
 ps               1.6.0   2021-02-28 [1] CRAN (R 3.6.1)
 purrr            0.3.4   2020-04-17 [1] CRAN (R 3.6.1)
 R6               2.5.0   2020-10-28 [1] CRAN (R 3.6.1)
 RColorBrewer     1.1-2   2014-12-07 [1] CRAN (R 3.6.1)
 Rcpp             1.0.6   2021-01-15 [1] CRAN (R 3.6.1)
 readxl           1.3.1   2019-03-13 [1] CRAN (R 3.6.1)
 remotes          2.2.0   2020-07-21 [1] CRAN (R 3.6.1)
 rlang            0.4.10  2020-12-30 [1] CRAN (R 3.6.1)
 rmarkdown        2.7     2021-02-19 [1] CRAN (R 3.6.1)
 rprojroot        2.0.2   2020-11-15 [1] CRAN (R 3.6.1)
 RSQLite          2.2.5   2021-03-27 [1] CRAN (R 3.6.1)
 rstudioapi       0.13    2020-11-12 [1] CRAN (R 3.6.1)
 S4Vectors      * 0.24.4  2020-04-09 [1] Bioconductor  
 sass             0.3.1   2021-01-24 [1] CRAN (R 3.6.1)
 scales         * 1.1.1   2020-05-11 [1] CRAN (R 3.6.1)
 sessioninfo      1.1.1   2018-11-05 [1] CRAN (R 3.6.1)
 stringi          1.5.3   2020-09-09 [1] CRAN (R 3.6.1)
 stringr          1.4.0   2019-02-10 [1] CRAN (R 3.6.1)
 superheat      * 0.1.0   2017-02-04 [1] CRAN (R 3.6.1)
 testthat         3.0.2   2021-02-14 [1] CRAN (R 3.6.1)
 tibble           3.1.0   2021-02-25 [1] CRAN (R 3.6.1)
 tidyr            1.1.3   2021-03-03 [1] CRAN (R 3.6.1)
 tidyselect       1.1.0   2020-05-11 [1] CRAN (R 3.6.1)
 usethis          2.0.1   2021-02-10 [1] CRAN (R 3.6.1)
 utf8             1.2.1   2021-03-12 [1] CRAN (R 3.6.1)
 vctrs            0.3.6   2020-12-17 [1] CRAN (R 3.6.1)
 viridisLite      0.3.0   2018-02-01 [1] CRAN (R 3.6.1)
 withr            2.4.1   2021-01-26 [1] CRAN (R 3.6.1)
 xfun             0.22    2021-03-11 [1] CRAN (R 3.6.1)
 xml2             1.3.2   2020-04-23 [1] CRAN (R 3.6.1)
 yaml             2.2.1   2020-02-01 [1] CRAN (R 3.6.1)
 zip              2.1.1   2020-08-27 [1] CRAN (R 3.6.1)
 zlibbioc         1.32.0  2019-10-29 [1] Bioconductor  

[1] /homedirs26/sghms/bms/users/anohturf/R/x86_64-pc-linux-gnu-library/3.6
[2] /opt/R/3.6.1/lib64/R/library
---
title: "A novel role for alkyladenine DNA glycosylase in regulating alkylation-induced ER stress"
subtitle: "Figure S1"
author: "L Milano, CF Charlier, R Andreguetti, E Healing, MP Thomé, R Elliott, JY Masson, LD Samson, G Lenz, JAP Henriques, A Nohturfft and LB Meira"
output:
  html_notebook:
    toc: TRUE
    toc_float: TRUE
    toc_depth: 3
    code_folding: "hide"
    number_sections: FALSE
    theme: "readable"
    highlight: "tango"
    fig_caption: TRUE
    css: "./source/styles.css"
---

```{js}
function myFunction(id) {
  var x = document.getElementById(id);
  if (x.style.display === 'none') {
    x.style.display = 'block';
  } else {
    x.style.display = 'none';
  }
}
```

<div class='comments'>
Note: This analysis requires input data generated by an earlier script.
</div>

## Setup  

* Clear memory and console  
* Load packages  
* Get current script name  

```{r CLEAR MEMORY AND PACKAGES AND CONSOLE, results="hide"}
# Clear memory
rm(list=setdiff(ls(all=TRUE), c(".Random.seed")))
# Clear console
cat("\014")
```

## Set up  
```{r Packages, include=FALSE, results="hide"}
library(dplyr)
library(magrittr)
library(ggplot2)
library(plotly)
```


```{r Get script name,  class.output="txt_output"}
this.script <- rstudioapi::getActiveDocumentContext()$path %>% basename
cat("Script:", this.script)
```


```{r Functions_1}
gg.get.breaks_by1 <- function(limits) {
  a <- floor(limits[1])
  b <- ceiling(limits[2])
  seq(a, b, by = 1)
}

gg.get.breaks_by2 <- function(limits) {
  a <- floor(limits[1])
  if (a %% 2 == 1) a <- a - 1
  b <- ceiling(limits[2])
  if (b %% 2 == 1) b <- b + 1
  seq(a, b, by = 2)
}
```


```{r Functions_2}
get.gg <- function(mtrx, colum.names, axis.labs, titre,
                    marker.col.false, marker.col.true="#B20000", marker.alpha=1, ttable) {

  ttable2 <- dplyr::select(ttable, PROBEID, SYMBOL, adj.P.Val) %>% 
    dplyr::group_by(PROBEID) %>%
    dplyr::slice(which.min(adj.P.Val)) %>% 
    dplyr::ungroup(.)

  df <- mtrx[,colum.names] %>% 
    magrittr::set_colnames(axis.labs) %>% 
    tibble::rownames_to_column("PROBEID")
  
  df2 <- merge(x=ttable2, y=df, by="PROBEID", all.x=FALSE, all.y=TRUE) %>% 
    dplyr::mutate(p.ok = adj.P.Val <= 0.05) %>% 
    dplyr::mutate(p.ok = as.character(p.ok)) %>% 
    dplyr::mutate(p.ok = factor(p.ok, levels=c("FALSE", "TRUE"))) %>% 
    dplyr::mutate(ID = paste0(SYMBOL, " (", PROBEID, ")")) %>% 
    dplyr::select(-PROBEID, -SYMBOL, -adj.P.Val) %>% 
    dplyr::select(ID, everything())

  res <- df2 %>% 
    ggplot(aes_string(x=axis.labs[1], y=axis.labs[2])) +
    geom_point(aes(text=ID, color=p.ok), alpha=marker.alpha) + # color=marker.col.false,
    # scale_color_discrete(name="p ≤ 0.05") +
    scale_color_manual(values=c(marker.col.false, marker.col.true), name="p ≤ 0.05") +
    scale_x_continuous(limits =c(min(mtrx), max(mtrx))) +
    scale_y_continuous(limits =c(min(mtrx), max(mtrx))) +
    geom_abline(intercept = 0, slope = 1, color="grey25") +
    geom_abline(intercept = -log2(1.75), slope = 1, color="grey25", linetype="dashed") +
    geom_abline(intercept = log2(1.75), slope = 1, color="grey25", linetype="dashed") +
    labs(title = titre)
  res
}
```

## Load data
### Load mean expression data  
Table includes probe IDs, gene symbols and gene names.  
Probe IDs and symbols are not unique (due to gene symbol mapping).  

```{r Read mean expr, class.output="txt_output"}
mean.expr.file <- "./data/01_Raw_Data_Processing.Rmd.expr_mean.txt"
matrix.df.mean <- read.table(mean.expr.file, sep="\t", header=T,
                             stringsAsFactors = FALSE) %>% 
  dplyr::select(-SYMBOL, -GENENAME) %>% 
  dplyr::distinct(PROBEID, .keep_all=TRUE) %>% 
  tibble::column_to_rownames("PROBEID")
cat("File read:", mean.expr.file)
rm(mean.expr.file)
```

```{r include=FALSE}
head(matrix.df.mean)
```


### Load differentially expressed probes (effect of MMS)  
```{r Read DE probes, class.output="txt_output"}
# list.files("data")
diff.probes.file <- "./data/02_Suppl_Table_01.Rmd.DEprobes.RDS"
stopifnot(file.exists(diff.probes.file))
test.results.probes <- readRDS(diff.probes.file)
cat("File read:", diff.probes.file)
rm(diff.probes.file)
```

```{r results="asis"}
x <- paste(utils::capture.output(str(test.results.probes)), collapse="<br>\n", sep="")
details::details(x, summary="Show summary of DE probes", lang=NULL)
rm(x)
```


### Load toptables  
```{r Read toptables, class.output="txt_output"}
# list.files()
tt.file <- "Supplemental_Data_1.xlsx"
stopifnot(file.exists(tt.file))
tt.sheet.names <- openxlsx::getSheetNames(tt.file)
# tt.sheet.names
# "ko.ctrl.vs.wt.ctrl" "ko.mms.vs.wt.mms"   "wt.mms.vs.wt.ctrl"  "ko.mms.vs.ko.ctrl"
get.xlsx.data <- function(fil, sheet) {
  openxlsx::read.xlsx(xlsxFile = fil, sheet=sheet)
}
tt.list <- lapply(tt.sheet.names, function(the.sheet) {
  res <- get.xlsx.data(fil=tt.file, sheet=the.sheet) %>% 
    dplyr::select(PROBEID, SYMBOL, logFC, adj.P.Val)
  res
}) %>% 
  set_names(tt.sheet.names)
cat("File read:", tt.file)
rm(tt.file, tt.sheet.names)
```


```{r results="asis"}
y <- paste(utils::capture.output(str(tt.list)), collapse="<br>\n", sep="")
details::details(y, summary="Show summary of toptables list", lang=NULL)
rm(y)
```


## Plot probes suppressed OR induced by MMS in wild-type OR knockout    
### Select probes to plot
```{r Select genes to plot, class.output="txt_output"}
wt.ko.down.up <- sort(unique(unlist(test.results.probes)))
stopifnot(!any(is.na(wt.ko.down.up)))
cat("Probes selected:", length(wt.ko.down.up))
```



```{r results="hide"}
# matrix.df.mean[1:4,1:4]
mx.wt.ko.down.up <- matrix.df.mean[wt.ko.down.up,]
dim(mx.wt.ko.down.up)
```


```{r warning=FALSE, message=FALSE}
gg.6.4 <- get.gg(mtrx=mx.wt.ko.down.up,
       colum.names = c("Wt_Ctrl", "Ko_Ctrl"),
       axis.labs = c("WT", "KO"),
       titre = "Control: Knockout versus Wild-type",
       marker.col.false = "#005AB5", # bp[3]
       marker.col.true = "#DC3220",
       marker.alpha=0.5,
       ttable=tt.list$ko.ctrl.vs.wt.ctrl)
gg.6.4b <- gg.6.4 + 
  theme_classic() +
  coord_equal()
ggplotly(gg.6.4b)
```


```{r class.output="txt_output"}
out.file.pdf <- "Figure_S1.pdf"
ggsave(filename=out.file.pdf, plot = gg.6.4b, width = 10, height=10, units = "cm")
cat("Saved:", out.file.pdf)
```


### Session info  

<button class="button" onclick="myFunction('DIV_1')">Show/hide session info</button>
<div id="DIV_1" class="div_default_hide">

```{r SESSION INFO DATE, results="asis"}
cat("Date:", format(Sys.time(), "%a %d-%b-%Y %H:%M:%S"), "<br>\n")
```

```{r print_session_info, R.options=list(width=70)}
devtools::session_info()
```
</div>
  
```{js}
var divsToHide = document.getElementsByClassName("div_default_hide");
for(var i = 0; i < divsToHide.length; i++)
{
  divsToHide[i].style.display = 'none';
}
```



